Non-Overlapping Domain Decomposition Methods For Dual Total Variation Based Image Denoising

M. Hintermüller; A. Langer

doi:10.1007/s10915-014-9863-8

Non-Overlapping Domain Decomposition Methods For Dual Total Variation Based Image Denoising

M. Hintermüller, A. Langer

Research output: Contribution to journal › Article › peer-review

23 Citations (Scopus)

Abstract

In this paper non-overlapping domain decomposition methods for the pre-dual total variation minimization problem are introduced. Both parallel and sequential approaches are proposed for these methods for which convergence to a minimizer of the original problem is established. The associated subproblems are solved by a semi-smooth Newton method. Several numerical experiments are presented, which show the successful application of the sequential and parallel algorithm for image denoising.

Original language	English
Pages (from-to)	456–481
Number of pages	26
Journal	Journal of Scientific Computing
Volume	62
Early online date	7 May 2014
DOIs	https://doi.org/10.1007/s10915-014-9863-8
Publication status	Published - Feb 2015
Externally published	Yes

Keywords

Domain decomposition
Subspace correction
Total bounded variation
Pre-dual
Convex optimization
Convergence analysis
Image reconstruction

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10.1007/s10915-014-9863-8

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abstract = "In this paper non-overlapping domain decomposition methods for the pre-dual total variation minimization problem are introduced. Both parallel and sequential approaches are proposed for these methods for which convergence to a minimizer of the original problem is established. The associated subproblems are solved by a semi-smooth Newton method. Several numerical experiments are presented, which show the successful application of the sequential and parallel algorithm for image denoising.",

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author = "M. Hinterm{\"u}ller and A. Langer",

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AU - Langer, A.

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AB - In this paper non-overlapping domain decomposition methods for the pre-dual total variation minimization problem are introduced. Both parallel and sequential approaches are proposed for these methods for which convergence to a minimizer of the original problem is established. The associated subproblems are solved by a semi-smooth Newton method. Several numerical experiments are presented, which show the successful application of the sequential and parallel algorithm for image denoising.

KW - Domain decomposition

KW - Subspace correction

KW - Total bounded variation

KW - Pre-dual

KW - Convex optimization

KW - Convergence analysis

KW - Image reconstruction

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SP - 456

EP - 481

JO - Journal of Scientific Computing

JF - Journal of Scientific Computing

ER -

Non-Overlapping Domain Decomposition Methods For Dual Total Variation Based Image Denoising

Abstract

Keywords

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